Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN, USA.
Department of Medicine, Vanderbilt University Medical Center, Nashville, TN, USA.
J Diabetes Sci Technol. 2024 Mar;18(2):423-429. doi: 10.1177/19322968221119788. Epub 2022 Sep 1.
The insulin ordering process is an opportunity to provide clinicians with hypoglycemia risk predictions, but few hypoglycemia models centered around the insulin ordering process exist.
We used data on adult patients, admitted in 2019 to non-ICU floors of a large teaching hospital, who had orders for subcutaneous insulin. Our outcome was hypoglycemia, defined as a blood glucose (BG) <70 mg/dL within 24 hours after ordering insulin. We trained and evaluated models to predict hypoglycemia at the time of placing an insulin order, using logistic regression, random forest, and extreme gradient boosting (XGBoost). We compared performance using area under the receiver operating characteristic curve (AUCs) and precision-recall curves. We determined recall at our goal precision of 0.30.
Of 21 052 included insulin orders, 1839 (9%) were followed by a hypoglycemic event within 24 hours. Logistic regression, random forest, and XGBoost models had AUCs of 0.81, 0.80, and 0.79, and recall of 0.44, 0.49, and 0.32, respectively. The most significant predictor was the lowest BG value in the 24 hours preceding the order. Predictors related to the insulin order being placed at the time of the prediction were useful to the model but less important than the patient's history of BG values over time.
Hypoglycemia within the next 24 hours can be predicted at the time an insulin order is placed, providing an opportunity to integrate decision support into the medication ordering process to make insulin therapy safer.
胰岛素医嘱流程是为临床医生提供低血糖风险预测的机会，但围绕胰岛素医嘱流程的低血糖预测模型很少。
我们使用了 2019 年在一家大型教学医院非 ICU 病房住院的成年患者的数据，这些患者有皮下胰岛素医嘱。我们的结局是低血糖，定义为胰岛素医嘱后 24 小时内血糖（BG）<70mg/dL。我们使用逻辑回归、随机森林和极端梯度提升（XGBoost）训练和评估了在下达胰岛素医嘱时预测低血糖的模型。我们通过接受者操作特征曲线下面积（AUCs）和精度-召回曲线比较了性能。我们在目标精度为 0.30 时确定了召回率。
在 21052 例纳入的胰岛素医嘱中，1839 例（9%）在 24 小时内发生低血糖事件。逻辑回归、随机森林和 XGBoost 模型的 AUC 分别为 0.81、0.80 和 0.79，召回率分别为 0.44、0.49 和 0.32。最显著的预测指标是医嘱前 24 小时内的最低 BG 值。与预测时下达胰岛素医嘱相关的预测指标对模型有用，但不如患者随时间推移的 BG 值历史重要。
可以在下达胰岛素医嘱时预测接下来 24 小时内的低血糖，为将决策支持整合到药物医嘱流程中以提高胰岛素治疗安全性提供了机会。
